Current diagnostic tests cannot reliably determine prostate cancer extent (volume and location) or biological aggressiveness. Our long term goal is to develop a non-invasive imaging technique that accurately assesses the clinical significance of prostate cancer and that can be used for diagnosis, treatment planning, and therapeutic monitoring. The main objective of this particular application is to realize the full potential of 3 Tesla MRI to generate cancer probability maps by combining the multi-parametric data generated from anatomic and functional studies within a new statistical model. Therefore, based on previous results from our group and others, the central hypothesis is that multi-parametric anatomic, vascular and metabolic data can determine the extent and aggressiveness of prostate cancer as validated by correlation with postoperative histopathologic determination of extent and tumor grade, and molecular assessment of aggressiveness. Supported by previous developments, this hypothesis will be tested with four specific aims: 1) generate parametric maps from MRI data acquired and processed with novel techniques; 2) develop and validate a 3-dimensional (3D) strategy to spatially co-register MRI images to histopathology sections from prostatectomy; 3) develop a classifier based on 3T MRI data to produce a 3D probability map of cancer; and 4) identify MRI features that predict histological and molecular markers of aggressiveness. Under the first two aims MRI data will be acquired and processed with developed methods to generate improved parametric maps which are then registered to reconstructed histopathology volumes. Under the third aim, the MRI parametric maps and histopathology results are used to train a statistical classifier to facilitate the generation of patient specific cancer probability maps. Finally, in the fourth aim, proven molecular markers of aggressiveness will be correlated with MRI, histopathology and standard clinical factors. The proposed work is innovative in several ways: 1) it will implement new acquisition and quantitation methods for DCE-MRI and 3DSI on a 3 Tesla system; 2) it will use novel and robust statistical modeling to simultaneously combine anatomic and MRI data to determine the extent of cancer, and 3) it will correlate MRI features with spatially registered histopathology and proven aggressiveness biomarkers. Our expected outcome is the development of a novel MRI-based imaging method to non-invasively and reliably determine both the extent and aggressiveness of prostate cancer. It is our hope that the methods developed in this study may permit doctors and their patients to make better treatment decisions and reduce morbidity and mortality due to prostate cancer.